This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets ...

Fuzzy logic programming is a growing declarative paradigm aiming to integrate fuzzy logic into logic programming. One of the most difficult tasks when specifying a fuzzy logic program is determining the right weights for each rule, as well as the most appropriate fuzzy connectives and operators. In this paper, we introduce a symbolic extension of fuzzy logic programs in which some of these para...

The pioneer work of image compression/reconstruction based on fuzzy relational equations (ICF) and the related works are introduced. The ICF regards an original image as a fuzzy relation by embedding the brightness level into [0,1]. The compression/reconstruction of ICF correspond to the composition/solving inverse problem formulated on fuzzy relational equations. Optimizations of ICF can be co...

This paper presents an incremental learning algorithm within the framework of a fuzzy intelligent system. The incremental learning algorithm is based on priority values attached to fuzzy rules. The priority value of a fuzzy rule is generated based on the fuzzy belief values of the fuzzy rule derived from the training data. The fuzzy incremental algorithm has three important properties. It can d...

In order to design a human resource management system, the Secure SHell, SSH, fuzzy framework is used in this paper for efficiency and high reliability. SSH fuzzy mathematics method plays a positive role in building the enterprise human resources management system. By evaluating production quota of an enterprise, we can make a scientific comparison in human resources management level between di...

In this paper, we present a tool to help reduce the uncertainty presented in the resource selection problem when information is subjective in nature. The candidates and the “ideal” resource required by evaluators are modeled by fuzzy subsets whose elements are trapezoidal fuzzy numbers (TrFN). By modeling with TrFN the subjective variables used to determine the best among a set of resources, on...

In this paper the conventional fuzzy k-modes algorithm for clustering categorical data is extended by representing the clusters of categorical data with fuzzy centroids instead of the hard-type centroids used in the original algorithm. Use of fuzzy centroids makes it possible to fully exploit the power of fuzzy sets in representing the uncertainty in the classification of categorical data. To t...

Fuzzy logic programming is a growing declarative paradigm aiming to integrate fuzzy logic into logic programming. One of the most di cult tasks when specifying a fuzzy logic program is determining the right weights for each rule, as well as the most appropriate fuzzy connectives and operators. In this paper, we introduce a symbolic extension of fuzzy logic programs in which some of these parame...

In this paper, we examine and compare the performance of four fuzzy rule generation methods on Wisconsin breast cancer data [2]. These methods were reported by Ishibuchi [1] et al. For the diagnosis of breast cancer, the determination of the presence of benign/malignant breast tumors represents a very complex problem (even for an experienced cytologist). The goal of this paper is to compare and...

In this study, a new modeling method based on trapezoidal fuzzy number FAHP has been proposed to solve the problem of risk element transmission in enterprise project evaluation chain. We firstly put forward enterprise project chain risk element transmission and constructs enterprise project chain risk element transmission tetrahedron model and then proposed the theory of enterprise project eval...